(Invited) Composition and Strain Engineering of New Group-IV Thermoelectric Materials
Bibliographic record
Abstract
Sequential integration of thermoelectric generators (TEGs) on a silicon wafer is essential for next-generation electronics such as internet-of-things sensing network as an energy-harvesting device. A guideline of thermoelectric performance is defined as dimensionless figure of merit, zT = S 2 σT / κ , which is calculated from Seebeck coefficient ( S ), electrical conductivity ( σ ), thermal conductivity ( κ ), and absolute temperature ( T ). Germanium tin (Ge 1 − x Sn x ) binary alloys theoretically possess a low κ [1] and a high σ [2], implying that Ge 1 − x Sn x enables high-performance TEGs. In our previous study on the thermal conductivity of Ge 1 − x Sn x epitaxial layer grown on (001)-oriented Ge [3], it was revealed that a very low κ of 2.0 W/mK was obtained for a Ga-doped Ge 0.929 Sn 0.071 epitaxial layer. The value is at least 77 and 30 times lower than that for bulk-Si (156 W/mK) and bulk-Ge (60 W/mK) [4], respectively, and same order with that for amorphous Si (1−2 W/mK) [5]. However, thermoelectric properties of the epitaxial layers could not be clarified because the underlying substrates were not electrical insulators. It is therefore, in this study, we aim to reveal thermoelectric properties of Ge 1 − x Sn x epitaxial layers with a wide range of Sn contents grown by low-temperature molecular beam epitaxy. Here, semi-insulating wafers of Si, GaAs, and InP were used as a substrate in order to isolate electrically from the substrates. Combined with the results for the polycrystalline films, we will establish a guideline for enhancing the zT value in narrow band-gap group-IV materials. Acknowledgments: This work was partially supported by Grants-in-Aid for Scientific Research (S) (Grant No. 26220605) and Young Scientists (A) (Grant No. 17H04919) from JSPS in Japan and PRESTO (Grant No. JPMJPR15R2) from JST in Japan. References: [1] S. N. Khatami and Z. Aksamiji, Phys. Rev. Appl. 6 , 014015 (2016). [2] K. L. Low et al. , J. Appl. Phys. 112 , 103715 (2012). [3] M. Kurosawa et al. , “Thermophysical characterizations of Ge 1 − x Sn x epitaxial layers aiming for thermoelectric device,” 9th International Conference on Silicon Epitaxy and Hetero-structures (ICSI-9), Montreal, Canada, 4.4.1, May 21, (2015). [4] C. J. Glassbrenner and G. A. Slack, Phys. Rev. 134 , A1058 (1964). [5] D. G. Cahill et al. , J. Vac. Sci. Technol. A 7 , 1259 (1989).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".